Summary
Summary {data-width=650}
Manhattan plot
manhattan_plot
QQ plot
qq_plot
AF plot
af_plot
P-Z plot
pz_plot
beta_std plot
beta_std_plot
Metadata
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"Gwas2VCF_command": "--data /data/cromwell-executions/qc/4e5b2aa7-25ef-4a08-b5b3-4a793e9463b4/call-vcf/inputs/-2071042873/upload.txt.gz --id bbj-a-159 --json /data/cromwell-executions/qc/4e5b2aa7-25ef-4a08-b5b3-4a793e9463b4/call-vcf/inputs/-2071042873/bbj-a-159_data.json --ref /data/cromwell-executions/qc/4e5b2aa7-25ef-4a08-b5b3-4a793e9463b4/call-vcf/inputs/1899004205/human_g1k_v37.fasta --dbsnp /data/cromwell-executions/qc/4e5b2aa7-25ef-4a08-b5b3-4a793e9463b4/call-vcf/inputs/-307190728/dbsnp.v153.b37.vcf.gz --out /data/igd/bbj-a-159/bbj-a-159.vcf.gz --alias alias.txt --cohort_cases 29319 --cohort_controls 183134; 1.3.0",
"file_date": "2022-01-15T02:01:55.554588",
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}
LDSC
*********************************************************************
* LD Score Regression (LDSC)
* Version 1.0.1
* (C) 2014-2019 Brendan Bulik-Sullivan and Hilary Finucane
* Broad Institute of MIT and Harvard / MIT Department of Mathematics
* GNU General Public License v3
*********************************************************************
Call:
./ldsc.py \
--h2 /data/cromwell-executions/qc/4e5b2aa7-25ef-4a08-b5b3-4a793e9463b4/call-ldsc/inputs/-2071042873/bbj-a-159.vcf.gz \
--ref-ld-chr /data/ref/eur_w_ld_chr/ \
--out /data/igd/bbj-a-159/ldsc.txt \
--w-ld-chr /data/ref/eur_w_ld_chr/
Beginning analysis at Sat Jan 15 02:18:22 2022
Reading summary statistics from /data/cromwell-executions/qc/4e5b2aa7-25ef-4a08-b5b3-4a793e9463b4/call-ldsc/inputs/-2071042873/bbj-a-159.vcf.gz ...
Read summary statistics for 8880965 SNPs.
Dropped 96123 SNPs with duplicated rs numbers.
Reading reference panel LD Score from /data/ref/eur_w_ld_chr/[1-22] ... (ldscore_fromlist)
Read reference panel LD Scores for 1290028 SNPs.
Removing partitioned LD Scores with zero variance.
Reading regression weight LD Score from /data/ref/eur_w_ld_chr/[1-22] ... (ldscore_fromlist)
Read regression weight LD Scores for 1290028 SNPs.
After merging with reference panel LD, 1071569 SNPs remain.
After merging with regression SNP LD, 1071569 SNPs remain.
Using two-step estimator with cutoff at 30.
Total Observed scale h2: 0.0583 (0.0068)
Lambda GC: 1.1864
Mean Chi^2: 1.3035
Intercept: 1.0508 (0.0118)
Ratio: 0.1675 (0.0389)
Analysis finished at Sat Jan 15 02:20:15 2022
Total time elapsed: 1.0m:52.76sQC metrics
Metrics
Metrics
{
"af_correlation": 0.9111,
"inflation_factor": 1.1556,
"mean_EFFECT": -0.0001,
"n": "-Inf",
"n_snps": 8881048,
"n_clumped_hits": 72,
"n_p_sig": 9751,
"n_mono": 0,
"n_ns": 822371,
"n_mac": 0,
"is_snpid_unique": false,
"n_miss_EFFECT": 0,
"n_miss_SE": 0,
"n_miss_PVAL": 0,
"n_miss_AF": 0,
"n_miss_AF_reference": 17308,
"n_est": "NA",
"ratio_se_n": "NA",
"mean_diff": "NaN",
"ratio_diff": "NaN",
"sd_y_est1": "NaN",
"sd_y_est2": "NA",
"r2_sum1": 0,
"r2_sum2": 0,
"r2_sum3": 0,
"r2_sum4": 0,
"ldsc_nsnp_merge_refpanel_ld": 1071569,
"ldsc_nsnp_merge_regression_ld": 1071569,
"ldsc_observed_scale_h2_beta": 0.0583,
"ldsc_observed_scale_h2_se": 0.0068,
"ldsc_intercept_beta": 1.0508,
"ldsc_intercept_se": 0.0118,
"ldsc_lambda_gc": 1.1864,
"ldsc_mean_chisq": 1.3035,
"ldsc_ratio": 0.1674
}
Flags
| name | value |
|---|---|
| af_correlation | FALSE |
| inflation_factor | FALSE |
| n | TRUE |
| is_snpid_non_unique | TRUE |
| mean_EFFECT_nonfinite | FALSE |
| mean_EFFECT_05 | FALSE |
| mean_EFFECT_01 | FALSE |
| mean_chisq | TRUE |
| n_p_sig | TRUE |
| miss_EFFECT | FALSE |
| miss_SE | FALSE |
| miss_PVAL | FALSE |
| ldsc_ratio | FALSE |
| ldsc_intercept_beta | FALSE |
| n_clumped_hits | FALSE |
| r2_sum1 | FALSE |
| r2_sum2 | FALSE |
| r2_sum3 | FALSE |
| r2_sum4 | FALSE |
Definitions
General metrics
af_correlation: Correlation coefficient betweenAFandAF_reference.inflation_factor(lambda): Genomic inflation factor.mean_EFFECT: Mean ofEFFECTsize.n: Maximum value of reported sample size across all SNPs, \(n\).n_clumped_hits: Number of clumped hits.n_snps: Number of SNPsn_p_sig: Number of SNPs with pvalue below5e-8.n_mono: Number of monomorphic (MAF == 1orMAF == 0) SNPs.n_ns: Number of SNPs with nonsense values:- alleles other than
A, C, G or T. - P-values
< 0or> 1. - negative or infinite standard errors (
<= 0or= Infinity). - infinite beta estimates or allele frequencies
< 0or> 1.
- alleles other than
n_mac: Number of cases whereMAC(\(2 \times N \times MAF\)) is less than6.is_snpid_unique:trueif the combination ofIDREFALTis unique and therefore no duplication in snpid.n_miss_<*>: Number ofNAobservations for<*>column.
se_n metrics
n_est: Estimated sample size value, \(\widehat{n}\).ratio_se_n: \(\texttt{ratio_se_n} = \frac{\sqrt{\widehat{n}}}{\sqrt{n}}\). We expectratio_se_nto be 1. When it is not 1, it implies that the trait did not have a variance of 1, the reported sample size is wrong, or that the SNP-level effective sample sizes differ markedly from the reported sample size.mean_diff: \(\texttt{mean_diff} = \sum_{j} \frac{\widehat{\beta_j^{std}} - \beta_j}{\texttt{n_snps}}\), mean difference between the standardised beta, predicted from P-values, and the observed beta. The difference should be very close to zero if trait has a variance of 1.- \(\widehat{\beta_j^{std}} = \sqrt{\frac{{z}_j^2 / ({z}_j^2 + n -2)}{2 \times {MAF}_j \times (1 - {MAF}_j)}} \times sign({z}_j)\),
- \({z}_j = \frac{\beta_j}{{se}_j}\),
- and \(\beta_j\) is the reported effect size.
ratio_diff: \(\texttt{ratio_diff} = |\frac{\texttt{mean_diff}}{\texttt{mean_diff2}}|\), absolute ratio between the mean ofdiffand the mean ofdiff2(expected difference between the standardised beta predicted from P-values, and the standardised beta derived from the observed beta divided by the predicted SD; NOT reported). The ratio should be close to 1. If different from 1, then implies that the betas are not in a standard deviation scale.- \(\texttt{mean_diff2} = \sum_{j} \frac{\widehat{\beta_j^{std}} - \beta^{\prime}_j}{\texttt{n_snps}}\)
- \(\beta^{\prime}_j = \frac{\beta_j}{\widehat{\texttt{sd2}}_{y}}\)
sd_y_est1: The standard deviation for the trait inferred from the reported sample size, median standard errors for the SNP-trait assocations and SNP variances.- \(\widehat{\texttt{sd1}}_{y} = \frac{\sqrt{n} \times median({se}_j)}{C}\),
- \(C = median(\frac{1}{\sqrt{2 \times {MAF}_j \times (1 - {MAF}_j)}})\),
- and \({se}_j\) is the reported standard error.
sd_y_est2: The standard deviation for the trait inferred from the reported sample size, Z statistics for the SNP-trait effects (beta/se) and allele frequency.- \(\widehat{\texttt{sd2}}_{y} = median(\widehat{sd_j})\),
- \(\widehat{sd_j} = \frac{\beta_j}{\widehat{\beta_j^{std}}}\),
r2 metrics
Sum of variance explained, calculated from the clumped top hits sample.
r2_sum<*>:r2statistics under various assumptions1: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var1}}}\), \(\texttt{var1} = 1\).2: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var2}}}\), \(\texttt{var2} = {\widehat{\texttt{sd1}}_{y}}^2\),3: \(r^2 = \sum_j{\frac{2 \times \beta_j^2 \times {MAF}_j \times (1 - {MAF}_j)}{\texttt{var3}}}\), \(\texttt{var3} = {\widehat{\texttt{sd2}}_{y}}^2\),4: \(r^2 = \sum_j{\frac{F_j}{F_j + n - 2}}\), \(F = \frac{\beta_j^2}{{se}_j^2}\).
LDSC metrics
Metrics from LD regression
ldsc_nsnp_merge_refpanel_ld: Number of remaining SNPs after merging with reference panel LD.ldsc_nsnp_merge_regression_ld: Number of remaining SNPs after merging with regression SNP LD.ldsc_observed_scale_h2_{beta,se}Coefficient value and SE for total observed scale h2.ldsc_intercept_{beta,se}: Coefficient value and SE for intercept. Intercept is expected to be 1.ldsc_lambda_gc: Lambda GC statistics.ldsc_mean_chisq: Mean \(\chi^2\) statistics.ldsc_ratio: \(\frac{\texttt{ldsc_intercept_beta} - 1}{\texttt{ldsc_mean_chisq} - 1}\), the proportion of the inflation in the mean \(\chi^2\) that the LD Score regression intercepts ascribes to causes other than polygenic heritability. The value of ratio should be close to zero, though in practice values of 0.1-0.2 are not uncommon, probably due to sample/reference LD Score mismatch or model misspecification (e.g., low LD variants have slightly higher \(h^2\) per SNP).
Flags
When a metric needs attention, the flag should return TRUE.
af_correlation:abs(af_correlation)< 0.7.inflation_factor:inflation_factor> 1.2.n:n(max reported sample size) < 10000.is_snpid_non_unique: NOTis_snpid_unique.mean_EFFECT_nonfinite:mean(EFFECT)isNA,NaN, orInf.mean_EFFECT_05:abs(mean(EFFECT))> 0.5.mean_EFFECT_01:abs(mean(EFFECT))> 0.1.mean_chisq:ldsc_mean_chisq> 1.3 orldsc_mean_chisq< 0.7.n_p_sig:n_p_sig> 1000.miss_<*>:n_miss_<*>/n_snps> 0.01.ldsc_ratio:ldsc_ratio> 0.5ldsc_intercept_beta:ldsc_intercept_beta> 1.5n_clumped_hits:n_clumped_hits> 1000r2_sum<*>:r2_sum<*>> 0.5
Plots
- Manhattan plot
- Red line: \(-log_{10}^{5 \times 10^{-8}}\)
- Blue line: \(-log_{10}^{5 \times 10^{-5}}\)
- QQ plot
- AF plot
- P-Z plot
- beta_std plot: Scatter plot between \(\widehat{\beta_j^{std}}\) and \(\beta_j\)
Diagnostics
Details
Summary stats
| skim_type | skim_variable | n_missing | complete_rate | character.min | character.max | character.empty | character.n_unique | character.whitespace | logical.mean | logical.count | numeric.mean | numeric.sd | numeric.p0 | numeric.p25 | numeric.p50 | numeric.p75 | numeric.p100 | numeric.hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| character | ID | 4 | 0.9999995 | 3 | 12 | 0 | 8879870 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | REF | 0 | 1.0000000 | 1 | 99 | 0 | 37242 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| character | ALT | 0 | 1.0000000 | 1 | 342 | 0 | 24009 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| logical | N | 8881048 | 0.0000000 | NA | NA | NA | NA | NA | NaN | : | NA | NA | NA | NA | NA | NA | NA | NA |
| numeric | CHROM | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 8.837095e+00 | 6.010889e+00 | 1.0000e+00 | 4.000000e+00 | 8.000000e+00 | 1.300000e+01 | 2.300000e+01 | ▇▅▅▂▂ |
| numeric | POS | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 7.945850e+07 | 5.554523e+07 | 7.9140e+03 | 3.374156e+07 | 7.078466e+07 | 1.147556e+08 | 2.492139e+08 | ▇▇▅▂▁ |
| numeric | EFFECT | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | -1.029000e-04 | 6.858380e-02 | -2.3692e+00 | -1.262350e-02 | 4.710000e-05 | 1.262210e-02 | 3.950770e+00 | ▁▇▁▁▁ |
| numeric | SE | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 3.513890e-02 | 6.006920e-02 | 7.2299e-03 | 1.058100e-02 | 1.442440e-02 | 3.157100e-02 | 1.966720e+00 | ▇▁▁▁▁ |
| numeric | PVAL | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.759094e-01 | 2.959636e-01 | 0.0000e+00 | 2.139760e-01 | 4.684147e-01 | 7.319156e-01 | 9.999999e-01 | ▇▇▆▆▆ |
| numeric | PVAL_ztest | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 4.759094e-01 | 2.959636e-01 | 0.0000e+00 | 2.139756e-01 | 4.684146e-01 | 7.319152e-01 | 9.999999e-01 | ▇▇▆▆▆ |
| numeric | AF | 0 | 1.0000000 | NA | NA | NA | NA | NA | NA | NA | 2.645015e-01 | 2.773025e-01 | 2.3500e-05 | 2.766190e-02 | 1.599820e-01 | 4.372070e-01 | 9.999760e-01 | ▇▂▂▁▁ |
| numeric | AF_reference | 17308 | 0.9980511 | NA | NA | NA | NA | NA | NA | NA | 2.523869e-01 | 2.494311e-01 | 1.9970e-04 | 3.793930e-02 | 1.735220e-01 | 4.055510e-01 | 9.998000e-01 | ▇▃▂▁▁ |
Head and tail
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 751343 | rs28544273 | T | A | -0.0047592 | 0.0150023 | 0.7510641 | 0.7510655 | 0.140648 | 0.242612 | NA |
| 1 | 751756 | rs143225517 | T | C | -0.0047432 | 0.0149433 | 0.7509275 | 0.7509281 | 0.139886 | 0.242212 | NA |
| 1 | 752566 | rs3094315 | G | A | 0.0029784 | 0.0129451 | 0.8180301 | 0.8180307 | 0.842543 | 0.718251 | NA |
| 1 | 752894 | rs3131971 | T | C | 0.0038746 | 0.0143281 | 0.7868392 | 0.7868396 | 0.857050 | 0.753195 | NA |
| 1 | 753405 | rs3115860 | C | A | 0.0044290 | 0.0147003 | 0.7631958 | 0.7631951 | 0.857362 | 0.751797 | NA |
| 1 | 753425 | rs3131970 | T | C | 0.0041701 | 0.0145775 | 0.7748292 | 0.7748285 | 0.856128 | 0.749002 | NA |
| 1 | 755890 | rs3115858 | A | T | 0.0043740 | 0.0149126 | 0.7692863 | 0.7692870 | 0.856987 | 0.751398 | NA |
| 1 | 756604 | rs3131962 | A | G | 0.0043756 | 0.0148734 | 0.7686135 | 0.7686130 | 0.853035 | 0.748003 | NA |
| 1 | 757734 | rs4951929 | C | T | 0.0044194 | 0.0150135 | 0.7684825 | 0.7684820 | 0.853015 | 0.748203 | NA |
| 1 | 757936 | rs4951862 | C | A | 0.0044206 | 0.0150153 | 0.7684471 | 0.7684469 | 0.853564 | 0.748802 | NA |
| CHROM | POS | ID | REF | ALT | EFFECT | SE | PVAL | PVAL_ztest | AF | AF_reference | N |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 23 | 154789496 | rs5940513 | A | G | -0.0061707 | 0.0094834 | 0.5152547 | 0.5152545 | 0.7785840 | 0.6378810 | NA |
| 23 | 154803410 | rs368676291 | T | C | -0.0063806 | 0.0099180 | 0.5200092 | 0.5200096 | 0.7571260 | 0.5353640 | NA |
| 23 | 154803411 | rs373288103 | G | A | -0.0063850 | 0.0099232 | 0.5199349 | 0.5199347 | 0.7572230 | 0.5353640 | NA |
| 23 | 154803415 | rs112167337 | A | C | -0.0062718 | 0.0099467 | 0.5283394 | 0.5283395 | 0.7556550 | 0.5345700 | NA |
| 23 | 154803793 | rs5940526 | T | A | -0.0063034 | 0.0099632 | 0.5269471 | 0.5269465 | 0.7549870 | 0.5311260 | NA |
| 23 | 154804301 | rs5940397 | G | A | -0.0065714 | 0.0100747 | 0.5142295 | 0.5142305 | 0.7712950 | 0.6442380 | NA |
| 23 | 154805102 | rs12834147 | G | C | -0.0066096 | 0.0101206 | 0.5136981 | 0.5137003 | 0.7665930 | 0.6360260 | NA |
| 23 | 154805911 | rs2890132 | G | A | -0.0065379 | 0.0101452 | 0.5192948 | 0.5192954 | 0.7680110 | 0.6378810 | NA |
| 23 | 154812754 | rs183018361 | G | T | -0.0255059 | 0.1055590 | 0.8090687 | 0.8090692 | 0.0014599 | 0.0230464 | NA |
| 23 | 154821956 | rs5940536 | G | A | -0.0195862 | 0.0985861 | 0.8425204 | 0.8425201 | 0.0014476 | 0.0389404 | NA |
bcf preview
1 751343 rs28544273 T A . PASS AF=0.140648 ES:SE:LP:AF:ID -0.00475925:0.0150023:0.124323:0.140648:rs28544273
1 751756 rs28527770 T C . PASS AF=0.139886 ES:SE:LP:AF:ID -0.00474324:0.0149433:0.124402:0.139886:rs28527770
1 752566 rs3094315 G A . PASS AF=0.842543 ES:SE:LP:AF:ID 0.00297839:0.0129451:0.0872307:0.842543:rs3094315
1 752894 rs3131971 T C . PASS AF=0.85705 ES:SE:LP:AF:ID 0.00387456:0.0143281:0.104114:0.85705:rs3131971
1 753405 rs3115860 C A . PASS AF=0.857362 ES:SE:LP:AF:ID 0.00442902:0.0147003:0.117364:0.857362:rs3115860
1 753425 rs1431026348 T C . PASS AF=0.856128 ES:SE:LP:AF:ID 0.00417011:0.0145775:0.110794:0.856128:rs1431026348
1 755890 rs1280367067 A T . PASS AF=0.856987 ES:SE:LP:AF:ID 0.00437398:0.0149126:0.113912:0.856987:rs1280367067
1 756604 rs3131962 A G . PASS AF=0.853035 ES:SE:LP:AF:ID 0.0043756:0.0148734:0.114292:0.853035:rs3131962
1 757734 rs1557551770 C T . PASS AF=0.853015 ES:SE:LP:AF:ID 0.00441939:0.0150135:0.114366:0.853015:rs1557551770
1 757936 rs1360886751 C A . PASS AF=0.853564 ES:SE:LP:AF:ID 0.00442061:0.0150153:0.114386:0.853564:rs1360886751
1 758144 rs3131956 A G . PASS AF=0.848328 ES:SE:LP:AF:ID 0.00410528:0.0145797:0.10887:0.848328:rs3131956
1 758626 rs3131954 C T . PASS AF=0.854053 ES:SE:LP:AF:ID 0.00447424:0.0152714:0.113771:0.854053:rs3131954
1 759837 rs3115851 T A . PASS AF=0.853887 ES:SE:LP:AF:ID 0.00483391:0.015484:0.122111:0.853887:rs3115851
1 760912 rs1048488 C T . PASS AF=0.843391 ES:SE:LP:AF:ID 0.00419354:0.0147195:0.110293:0.843391:rs1048488
1 761147 rs3115850 T C . PASS AF=0.842837 ES:SE:LP:AF:ID 0.00421969:0.0147336:0.110938:0.842837:rs3115850
1 761752 rs1057213 C T . PASS AF=0.851603 ES:SE:LP:AF:ID 0.0048812:0.0156405:0.122068:0.851603:rs1057213
1 845635 rs117086422 C T . PASS AF=0.134319 ES:SE:LP:AF:ID -0.0124913:0.0151716:0.38688:0.134319:rs117086422
1 846078 rs778265812 C T . PASS AF=0.133676 ES:SE:LP:AF:ID -0.0125603:0.0149422:0.397315:0.133676:rs778265812
1 846808 rs4475691 C T . PASS AF=0.141614 ES:SE:LP:AF:ID -0.0119384:0.0135799:0.420976:0.141614:rs4475691
1 846864 rs1269142199 G C . PASS AF=0.139575 ES:SE:LP:AF:ID -0.0121194:0.0138994:0.416523:0.139575:rs1269142199
1 847228 rs3905286 C T . PASS AF=0.146256 ES:SE:LP:AF:ID -0.0120227:0.014358:0.395346:0.146256:rs3905286
1 847491 rs1158719307 G A . PASS AF=0.14792 ES:SE:LP:AF:ID -0.0122401:0.0143592:0.404524:0.14792:rs1158719307
1 848090 rs4246505 G A . PASS AF=0.134694 ES:SE:LP:AF:ID -0.0127651:0.014863:0.408464:0.134694:rs4246505
1 848445 rs1156895099 G A . PASS AF=0.14934 ES:SE:LP:AF:ID -0.0123197:0.014479:0.403576:0.14934:rs1156895099
1 848456 rs11507767 A G . PASS AF=0.149775 ES:SE:LP:AF:ID -0.0123488:0.0145267:0.403089:0.149775:rs11507767
1 848654 rs5772026 TG T . PASS AF=0.131957 ES:SE:LP:AF:ID -0.0126818:0.015048:0.398633:0.131957:rs5772026
1 848738 rs3829741 C T . PASS AF=0.134247 ES:SE:LP:AF:ID -0.0125698:0.0148915:0.399442:0.134247:rs3829741
1 849440 rs79376265 C T . PASS AF=0.144507 ES:SE:LP:AF:ID 0.0083441:0.0160185:0.220091:0.144507:rs79376265
1 849863 rs61620633 G GACTGCCCAGCTC . PASS AF=0.152634 ES:SE:LP:AF:ID -0.0122258:0.0147775:0.389281:0.152634:rs61620633
1 850062 rs28723578 A T . PASS AF=0.150222 ES:SE:LP:AF:ID -0.0124247:0.0146275:0.402686:0.150222:rs28723578
1 850123 rs28622257 C T . PASS AF=0.135214 ES:SE:LP:AF:ID -0.0126942:0.0150155:0.400245:0.135214:rs28622257
1 850528 rs114889920 A G . PASS AF=0.145157 ES:SE:LP:AF:ID 0.00806859:0.0157802:0.215288:0.145157:rs114889920
1 851030 rs1284752010 G C . PASS AF=0.14182 ES:SE:LP:AF:ID 0.00873827:0.0157231:0.23779:0.14182:rs1284752010
1 851190 rs28609852 G A . PASS AF=0.148595 ES:SE:LP:AF:ID -0.0125671:0.0148551:0.400593:0.148595:rs28609852
1 851204 rs28552953 G C . PASS AF=0.159168 ES:SE:LP:AF:ID -0.0125805:0.0149978:0.396241:0.159168:rs28552953
1 852664 rs28605311 C T . PASS AF=0.138931 ES:SE:LP:AF:ID -0.0125517:0.0151978:0.388418:0.138931:rs28605311
1 852758 rs4970462 G C . PASS AF=0.133986 ES:SE:LP:AF:ID -0.0128801:0.0152495:0.399767:0.133986:rs4970462
1 853239 rs4970380 A G . PASS AF=0.134889 ES:SE:LP:AF:ID -0.0127736:0.0152753:0.394666:0.134889:rs4970380
1 853488 rs77644389 G A . PASS AF=0.147788 ES:SE:LP:AF:ID 0.00777921:0.0152711:0.214337:0.147788:rs77644389
1 854168 rs79188446 C T . PASS AF=0.148391 ES:SE:LP:AF:ID 0.00769356:0.0151557:0.213455:0.148391:rs79188446
1 855635 rs77712898 CCTT C . PASS AF=0.14973 ES:SE:LP:AF:ID 0.00748245:0.0148775:0.211119:0.14973:rs77712898
1 855774 rs77595185 G A . PASS AF=0.150021 ES:SE:LP:AF:ID 0.00745577:0.0148409:0.210843:0.150021:rs77595185
1 856436 rs34105146 TG T . PASS AF=0.118419 ES:SE:LP:AF:ID -0.0178142:0.0170802:0.527301:0.118419:rs34105146
1 857177 rs386627408 T C . PASS AF=0.438958 ES:SE:LP:AF:ID 0.0062344:0.0103728:0.261363:0.438958:rs386627408
1 857318 rs144110716 G GGAGT . PASS AF=0.150365 ES:SE:LP:AF:ID 0.00714083:0.0142647:0.209957:0.150365:rs144110716
1 857443 rs56663360 C T . PASS AF=0.173931 ES:SE:LP:AF:ID 0.00756668:0.014352:0.22327:0.173931:rs56663360
1 858040 rs4970460 C A . PASS AF=0.151389 ES:SE:LP:AF:ID -0.010047:0.0157958:0.280053:0.151389:rs4970460
1 858051 rs4970459 C T . PASS AF=0.153345 ES:SE:LP:AF:ID -0.00979319:0.0156499:0.274524:0.153345:rs4970459
1 858149 rs77520260 C T . PASS AF=0.150188 ES:SE:LP:AF:ID 0.00681571:0.0139974:0.203211:0.150188:rs77520260
1 858691 rs34628185 TG T . PASS AF=0.402711 ES:SE:LP:AF:ID -0.000322821:0.0105606:0.0107221:0.402711:rs34628185